Joint Bivariate Exponential Distribution

In summary, the problem involves two components and three types of shocks, with the times until the shocks occurring being independent exponential random variables. The joint bivariate exponential distribution of X_1 and X_2 is used to find the probability of X_1 and X_2 both being greater than certain values s and t. A potential approach is suggested in Yan, Carpenter, and Diawara (2006) and Diawara and Carpenter (2008).
  • #1
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Homework Statement
Consider two components and three types of shocks. A type 1 shock causes component 1 to fail, a type 2 shock causes component 2 to fail, and a type 3 shock causes both components 1 and 2 to fail. The times until shocks 1, 2, and 3 occur are independent exponential random variables with respective rates [itex]\lambda_1, \lambda_2, \lambda_3[/itex]. Let [itex]X_i[/itex] denote the time at which component i fails, i = 1, 2. The random variables [itex]X_1, X_2[/itex] are said to have a joint bivariate exponential distribution. Find [itex]P\{X_1 > s, X_2 > t\}[/itex].

The attempt at a solution
This problem would by so much easier if type 3 shocks didn't exists as it would make [itex]X_1, X_2[/itex] independent. Anywho...

Let [itex]Y_1, Y_2, Y_3[/itex] be the times shocks of type 1, 2, 3 occurred. I know I'm going to have to deal with the joint distribution of these three random variables. However, I can't think of anything. I need a little hint.
 
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  • #2
One idea is suggested in Yan, Carpenter and Diawara (2006) and in Diawara and Carpenter (2008) in their papers from AJMMS. Please check it out, and let me know if you have questions.
 

What is the Joint Bivariate Exponential Distribution?

The Joint Bivariate Exponential Distribution is a probability distribution that describes the joint behavior of two exponentially distributed random variables. It is used to model the relationship between two variables that have a constant hazard rate, meaning that their likelihood of occurrence does not change over time.

How is the Joint Bivariate Exponential Distribution different from the Bivariate Exponential Distribution?

The Joint Bivariate Exponential Distribution takes into account the correlation between the two variables, while the Bivariate Exponential Distribution assumes that the two variables are independent. This means that the Joint Bivariate Exponential Distribution is a more realistic model for situations where the two variables are related in some way.

What are the main properties of the Joint Bivariate Exponential Distribution?

The Joint Bivariate Exponential Distribution has two main properties: the marginal distributions of each individual variable are exponential, and the joint distribution is characterized by a correlation parameter. Additionally, the Joint Bivariate Exponential Distribution is a continuous distribution, meaning that the variables can take on any real value.

What are some applications of the Joint Bivariate Exponential Distribution?

The Joint Bivariate Exponential Distribution is commonly used in reliability analysis, where it is used to model the failure times of two components that are dependent on each other. It is also used in queuing theory, to model the arrival and service times of customers in a system. Additionally, it has applications in actuarial science and risk analysis.

How is the Joint Bivariate Exponential Distribution parameterized?

The Joint Bivariate Exponential Distribution is parameterized by the two scale parameters for each variable, as well as the correlation parameter. These parameters can be estimated from data using various statistical methods, such as maximum likelihood estimation. The correlation parameter can also be interpreted as a measure of the strength of the relationship between the two variables.

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